FLOAT32 NOT NULL -> FLOAT NOT NULL (#2180)

Author: BohuTANG

docs/en/guides/51-ai-functions/index.md

@@ -26,7 +26,7 @@ Embeddings are vector representations of text that capture semantic meaning. Sim
 
 ## Vector Storage in Databend
 
-Databend can store embedding vectors using the `ARRAY(FLOAT32 NOT NULL)` data type and perform similarity calculations with the cosine_distance function directly in SQL.
+Databend can store embedding vectors using the `ARRAY(FLOAT NOT NULL)` data type and perform similarity calculations with the cosine_distance function directly in SQL.
 
 ## Example: Document Similarity Search
 
@@ -36,7 +36,7 @@ CREATE TABLE articles (
     id INT,
     title VARCHAR,
     content VARCHAR,
-    embedding ARRAY(FLOAT32 NOT NULL)
+    embedding ARRAY(FLOAT NOT NULL)
 );
 
 -- Insert documents with embeddings

docs/en/sql-reference/20-sql-functions/11-ai-functions/02-ai-embedding-vector.md

@@ -50,7 +50,7 @@ CREATE TABLE documents (
                            id INT,
                            title VARCHAR,
                            content VARCHAR,
-                           embedding ARRAY(FLOAT32 NOT NULL)
+                           embedding ARRAY(FLOAT NOT NULL)
 );
 ```
 
@@ -72,7 +72,7 @@ UPDATE documents SET embedding = ai_embedding_vector(content) WHERE embedding IS
 
 After running the query, the embedding column in the table will contain the generated embeddings.
 
-The embeddings are stored as an array of `FLOAT32` values in the embedding column, which has the `ARRAY(FLOAT32 NOT NULL)` column type.
+The embeddings are stored as an array of `FLOAT` values in the embedding column, which has the `ARRAY(FLOAT NOT NULL)` column type.
 
 You can now use these embeddings for various natural language processing tasks, such as finding similar documents or clustering documents based on their content.
 

docs/en/sql-reference/20-sql-functions/11-vector-distance-functions/00-vector-cosine-distance.md

@@ -13,8 +13,8 @@ COSINE_DISTANCE(vector1, vector2)
 
 ## Arguments
 
-- `vector1`: First vector (ARRAY(FLOAT32 NOT NULL))
-- `vector2`: Second vector (ARRAY(FLOAT32 NOT NULL))
+- `vector1`: First vector (ARRAY(FLOAT NOT NULL))
+- `vector2`: Second vector (ARRAY(FLOAT NOT NULL))
 
 ## Returns
 
@@ -51,7 +51,7 @@ Create a table with vector data:
 ```sql
 CREATE OR REPLACE TABLE vectors (
     id INT,
-    vec ARRAY(FLOAT32 NOT NULL)
+    vec ARRAY(FLOAT NOT NULL)
 );
 
 INSERT INTO vectors VALUES

docs/en/sql-reference/20-sql-functions/11-vector-distance-functions/01-vector-l2-distance.md

@@ -13,8 +13,8 @@ L2_DISTANCE(vector1, vector2)
 
 ## Arguments
 
-- `vector1`: First vector (ARRAY(FLOAT32 NOT NULL))
-- `vector2`: Second vector (ARRAY(FLOAT32 NOT NULL))
+- `vector1`: First vector (ARRAY(FLOAT NOT NULL))
+- `vector2`: Second vector (ARRAY(FLOAT NOT NULL))
 
 ## Returns
 
@@ -51,7 +51,7 @@ Create a table with vector data:
 ```sql
 CREATE OR REPLACE TABLE vectors (
     id INT,
-    vec ARRAY(FLOAT32 NOT NULL)
+    vec ARRAY(FLOAT NOT NULL)
 );
 
 INSERT INTO vectors VALUES